A deep learning-based automated diagnosis system for SPECT myocardial perfusion imaging.

Images obtained from single-photon emission computed tomography for myocardial perfusion imaging (MPI SPECT) contain noises and artifacts, making cardiovascular disease diagnosis difficult. We developed a deep learning-based diagnosis support system using MPI SPECT images. Single-center datasets of MPI SPECT images (n = 5443) were obtained and labeled as healthy or coronary artery disease based on diagnosis reports. Three axes of four-dimensional datasets, resting, and stress conditions of three-dimensional reconstruction data, were reconstructed, and an AI model was trained to classify them. The trained convolutional neural network showed high performance [area under the curve (AUC) of the ROC curve: approximately 0.91; area under the recall precision curve: 0.87]. Additionally, using unsupervised learning and the Grad-CAM method, diseased lesions were successfully visualized. The AI-based automated diagnosis system had the highest performance (88%), followed by cardiologists with AI-guided diagnosis (80%) and cardiologists alone (65%). Furthermore, diagnosis time was shorter for AI-guided diagnosis (12 min) than for cardiologists alone (31 min). Our high-quality deep learning-based diagnosis support system may benefit cardiologists by improving diagnostic accuracy and reducing working hours.

Entrectinib Response to ROS1-Fusion-Positive Non-Small-Cell Lung Cancer That Progressed on Crizotinib with Leptomeningeal Metastasis: A Case Report.

Introduction: C-ros oncogene 1 (ROS1) translocation is an oncogenic driver-mutation identified in 1-2% of non-small-cell lung cancer (NSCLC) cases. Although crizotinib, a tyrosine kinase inhibitor (TKI) against ALK/ROS1, is known to be effective against ROS1-fusion-positive NSCLC, such cases sometimes progress with brain metastases. The most frequently reported crizotinib-resistance mutation is ROS1 G2032R, and some studies have found that even newly developed ROS1 TKIs, such as entrectinib and lorlatinib, show a decreased efficacy against it. The optimal therapies for ROS1-fusion-positive NSCLC and how such cases can be sequenced have not yet been established. Case Presentation: We herein report a patient with ROS1-fusion-positive NSCLC diagnosed at 34 years old. Crizotinib was started at the diagnosis and switched after 25 months to cisplatin/pemetrexed/bevacizumab once the disease progressed with multiple brain metastases that were resistant to stereotactic radiation therapy. The cytotoxic chemotherapy stabilized the patient's condition for 17 months until he developed leptomeningeal metastasis (LM). He underwent lumboperitoneal shunting and whole-brain radiotherapy, followed by crizotinib re-administration. Despite crizotinib treatment, his neurological symptoms, such as double vision, headache, weakness in the legs, and walking difficulties, progressed. Eventually, subsequent entrectinib treatment was initiated, which resolved all of the symptoms mentioned above. Regrettably, liquid next-generation sequencing had failed to detect the resistance mechanism due to minimal ctDNA in this case. Conclusion: These findings imply that sequential entrectinib administration may be effective in patients with disease progression limited to central nervous system metastases during crizotinib administration.

Anti-senescent drug screening by deep learning-based morphology senescence scoring.

Advances in deep learning technology have enabled complex task solutions. The accuracy of image classification tasks has improved owing to the establishment of convolutional neural networks (CNN). Cellular senescence is a hallmark of ageing and is important for the pathogenesis of ageing-related diseases. Furthermore, it is a potential therapeutic target. Specific molecular markers are used to identify senescent cells. Moreover senescent cells show unique morphology, which can be identified. We develop a successful morphology-based CNN system to identify senescent cells and a quantitative scoring system to evaluate the state of endothelial cells by senescence probability output from pre-trained CNN optimised for the classification of cellular senescence, Deep Learning-Based Senescence Scoring System by Morphology (Deep-SeSMo). Deep-SeSMo correctly evaluates the effects of well-known anti-senescent reagents. We screen for drugs that control cellular senescence using a kinase inhibitor library by Deep-SeSMo-based drug screening and identify four anti-senescent drugs. RNA sequence analysis reveals that these compounds commonly suppress senescent phenotypes through inhibition of the inflammatory response pathway. Thus, morphology-based CNN system can be a powerful tool for anti-senescent drug screening.